Digital image rendering of digital scenes has advanced to provide photo realistic results in a wide range of scenarios, such as movie production and industrial design. For example, it is now common to view digital images that include a photo realistic view of a digital scene in a fully animated movie, as an addition to a movie (e.g., an animated character), as standalone digital images, as part of advertisements, and so forth.
In order to provide these photo realistic results, techniques have been developed to enable computing devices to render a digital image that is consistent with an appearance of the digital scene in the real world. The computing devices achieve this by addressing a variety of characteristics of the digital scene that is to serve as a basis for rendering of the digital image. These characteristics include geometries of objects included in the scene, material surface properties of the objects, lighting, and position of a virtual “camera” that defines a viewpoint at which the digital scene is viewed.
A major factor in achievement of consistency of the appearance of the digital image with the real world is accurate simulation of light transport in the digital scene that follows actual physical laws of the real world. This includes how light is emitted from a light source and transported through the digital scene, including an effect of light as reflecting off objects in the scene and material and other properties of those objects which cause changes to the light. Simulation of this light transport as consistent with the real world thus involves addressing a variety of factors by the computing device to enable a digital image that is rendered of the digital scene to have a photo realistic appearance as if the digital image was taken in the real world.
One conventional technique that is widely used to accurately simulate light transport in the digital scene is referred to as a Monte Carlo technique. The Monte Carlo technique is viewed in industry as the standard solution used to achieve photo realistic results with sufficient reliability and high fidelity, e.g., for use in animated movies.
A drawback of the Monte Carlo technique, however, is its extremely high computational cost, which limits availability of this technique. In order to simulate light transport in the digital scene, for instance, the Monte Carlo technique employs a computing device to generate light path samples that connect light sources to a “camera” that represents a viewpoint of the scene that is used as a basis to render the digital image. Light transport is computed from those light paths by the computing device to obtain a final color for each pixel of the digital image to “capture” the digital image scene. If the sample size (i.e., the number of light paths) is small, the resulting rendered digital image typically exhibits a large amount of noise. Accordingly, in practice a multitude of light paths are sampled to render the digital image, thereby increasing a computation load in this rendering by the computing device. This is further exacerbated by complexity of the digital scene due to objects and material properties of those objects in order to calculate an effect of each of the light paths on light transport within the digital scene. Consequently, use of these conventional techniques is typically limited to entities that have a significant amount of computational resources available. Further, even when available, these conventional techniques may still take a significant amount of time to perform, e.g., to render individual digital images as frames of a movie.